Event-Driven State Estimation for Stochastic Networked Systems

Huang, Cong/ Mei, Peng/ Karimi, Hamid Reza

Academic Press Inc（2025/12発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 244 p.
• 言語 ENG
• 商品コード 9780443450143
• DDC分類 621.382

Full Description

Event-Driven State Estimation for Stochastic Networked Systems offers a comprehensive and clear explanation of recent developments in event-based state estimation for stochastic systems within limited communication networks, bringing together existing and emerging concepts. It provides a series of the latest results in, including but not limited to, recursive state estimation, fusion estimation, and state and fault estimation. The book provides practitioner readers with practical tools for the analysis and design of stochastic systems under limited communication networks, capturing recent advances in theory, technological aspects and real-world applications of advanced event-based state estimation methodologies. Realistic research problems are addressed in each chapter, with numerical and simulation results provided to reflect engineering practice, while demonstrating the main focus of the developed estimation approaches. The book is an advanced-level resource presented in an accessible manner, appealing to senior students as a core reference and to researchers and practitioners alike

Contents

1. Introduction
2. State-saturated resilient filtering for nonlinear complex networks under event-triggering protocols
3. A Dynamically event-triggered approach to recursive filtering with censored measurements and parameter uncertainties
4. Distributed state-of-charge estimation for lithium-ion batteries with random sensor failure under dynamic event-triggering protocol
5. Event-based fusion estimation for multi-rate systems subject to sensor degradations
6. Event-triggering robust fusion estimation for a class of multi-rate systems subject to censored observations
7. Dynamic event-triggering joint state and unknown input estimation for nonlinear systems with random sensor failure
8. State and fault estimation for nonlinear systems subject to censored measurements: a dynamic event-triggered case
9. Event-triggering state and fault estimation for a class of nonlinear systems subject to sensor saturations